• Communications for Statistical Applications and Methods
• /
• v.20 no.6
• /
• pp.439-454
• /
• 2013

This study presents a Bayesian multiple change-point detection approach to segment and classify the observations that no longer come from an initial population after a certain time. Inferences are based on the multiple change-points in a sequence of random variables where the probability distribution changes. Bayesian multiple change-point estimation is classifies each observation into a segment. We use a truncated Poisson distribution for the number of change-points and conjugate prior for the exponential family distributions. The Bayesian method can lead the unsupervised classification of discrete, continuous variables and multivariate vectors based on latent class models; therefore, the solution for change-points corresponds to the stochastic partitions of observed data. We demonstrate segmentation with real data.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.33A no.4
• /
• pp.27-36
• /
• 1996

It is well known that the viterbi algorithm proposed as a mthod of decoding convolutional codes is in fact maximum likelihood (ML) and therefore optimal. But, because hardware complexity grows exponentially with the constraint length, there will be severe constraints on the implementation of the viterbi decoders. In this paper, the three-layered backpropagation neural networks are proposed as an alternative in order to get sufficiently useful performance and deal successfully with the problems of the viterbi decoder. This paper shows that the neural convolutional decoder (NCD) can make a decision in the point of ML in decoding and describes simulation results. The cause of the difference between stochastic results and simulation results is discussed, and then thefuture prospect of the NCD is described on the basis of the characteristic of the transfer function.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2023.05a
• /
• pp.254-255
• /
• 2023

This paper discovers a robust managerial strategy for a stochastic inventory of perishable products, where the model experiences changing factors including inner parameters and an external disturbance with unknown form. An analytical solution for the optimization problem can be obtained by applying the Hamilton-Bellman-Jacobi equation, however the policy result cannot completely suppress the oscillation from the external disturbance. Therefore, an intelligent approach named Radial Basis Function Neural Networks is applied to estimate the unknown disturbance and provide a robust controller to manipulate the inventory level more effective. The final results show the outstanding performance of RBFNN controller, where both the estimation error and control error are guaranteed in the predefined limit.

• Journal of the Society of Naval Architects of Korea
• /
• v.31 no.1
• /
• pp.142-154
• /
• 1994

In this paper, stochastic imperfection sensitivity analyses of stiffened cylindrical shells under static load are presented. Multimode formulation is performed for the buckling load calculation based on the Donnell's theory and Galerkin approximation. Random imperfection field theory and response surface method are combined with deterministic bucking analysis scheme to perform stochastic imperfection sensitivity analyses of stiffened cylindrical shells considering random geometric imperfection. From the characteristics of probabilistic bucking load, the relation between reliability index and safety parameter can be obtained in addition to the relation between load and reliability index. Those results can be used to determine the range of required safety parameter and acceptable imperfection.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.26 no.4
• /
• pp.296-309
• /
• 2022

Timer options are one of the contingent claims that, for given the variance budget, its payoff depends on a random maturity in terms of the realized variance unlike the standard European vanilla option with a fixed time maturity. Since it was first launched by Société Générale Corporate and Investment Banking in 2007, the valuation of the timer options under several stochastic environment for the volatility has been conducted by many researches. In this study, we propose the pricing of timer power options combined with standard timer options and the index of the power to the underlying asset for the investors to actualize lower risks and higher returns at the same time under the uncertain markets. By using the asymptotic analysis, we obtain the first-order approximation of timer power options. Moreover, we demonstrate that our solution has been derived accurately by comparing it with the solution from the Monte-Carlo method. Finally, we analyze the impact of the stochastic volatility with regards to various parameters on the timer power options numerically.

• The Korean Journal of Applied Statistics
• /
• v.14 no.2
• /
• pp.333-344
• /
• 2001

통계적 추론에 사용되는 많은 통계량들은 평균벡터의 평활함수의 형태로 표현이 가능하다. 본 연구에서는 이들 통계량들의 분포함수에 대한 안부점근사법을 제시하였다. 이 방법은 Na(1998)에서 제시된 일반적 통계량의 분포함수에 대한 안부점근사법이 평균벡터의 평활함수모형에 특히 유용하게 사용될 수 있음을 보인 것이다. 이 근사법은 정규근사에 비해 근사의 정도가 뛰어나며, 특히 통계량의 꼬리부분의 확률에 대해서도 정확도가 그대로 유지되는 장점이 있어 정밀한 추론이 요구되는 많은 문제에 효과적으로 사용될 수 있다. 모의 실험에 사용할 평균벡터의 평활함수 모형으로는 스튜던트화 분산을 고려하였다.

• Communications for Statistical Applications and Methods
• /
• v.24 no.5
• /
• pp.481-492
• /
• 2017

This paper investigates a convergence rate of a test statistics given by two scale sampling method based on $A\ddot{i}t$-Sahalia and Jacod (Annals of Statistics, 37, 184-222, 2009). This statistics tests for longitudinal data having the existence of long memory dependence driven by fractional Brownian motion with Hurst parameter $H{\in}(1/2,\;1)$. We obtain an upper bound in the Kolmogorov distance for normal approximation of this test statistic. As a main tool for our works, the recent results in Nourdin and Peccati (Probability Theory and Related Fields, 145, 75-118, 2009; Annals of Probability, 37, 2231-2261, 2009) will be used. These results are obtained by employing techniques based on the combination between Malliavin calculus and Stein's method for normal approximation.

• International Journal of Aeronautical and Space Sciences
• /
• v.18 no.4
• /
• pp.757-766
• /
• 2017

For implementing the proportional navigation guidance law on passive homing missiles equipped with strapdown imaging infrared seekers, the line-of-sight angles and rates with respect to the inertial frame should be estimated by carefully handling the parasitic instability effect due to the seeker's latency. By introducing a new state vector representation along with the Pade approximation for compensating the time-delay of the seeker, this paper proposes a new guidance filter structure, stochastic dynamic models and measurement equations, in three-dimensional homing problem. Then, it derives the line-of-sight angle and rate estimator in general two-dimensional engagement by applying the extended Kalman filter to the proposed structure. The estimation performance and the characteristics of the proposed filter were evaluated via a series of numerical experiments.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1995.09a
• /
• pp.99-107
• /
• 1995

The Phase-type(PH) distribution, defined as a distribution of the time until the absorption in a finite continuous-time Markov chain state with one absorbing state, has been widely used for various stochastic modelling. But great computational burdens often make us hesitate to apply PH methods. In this paper, we propose a seemingly efficient approximation method for phase type distributions. We first describe methods to bound the first passage time distribution in continuous-time Markov chains. Next, we adapt these bounding methods to approximate phase-tupe distributions. Numerical computation results are given to verify their efficiency.

• Proceedings of the IEEK Conference
• /
• 1998.06a
• /
• pp.106-109
• /
• 1998

Wireless communication systems, in particular, must operate in a crowded electro-magnetic environmnet where in-band undesired signals are treated as noise by the receiver. These interfering signals are often random but not Gaussian Due to nongaussian noise, the distribution of the observables cannot be specified by a finite set of parameters; instead r-dimensioal sample space (pure noise samples) is equiprobably partitioned into a finite number of disjointed regions using quantiles and a vector quantizer based on training samples. If we assume that the detected symbols are correct, then we can observe the pure noise samples during the training and transmitting mode. The algorithm proposed is based on a piecewise approximation to a regression function based on quantities and conditional partition moments which are estimated by a RMSA (Robbins-Monro Stochastic Approximation) algorithm. In this paper, we develop a diversity combiner with modified detector, called Non-Linear Detector, and the receiver has a differential phase detector in each diversity branch and at the combiner each detector output is proportional to the second power of the envelope of branches. Monte-Carlo simulations were used as means of generating the system performance.